Triple

T18426608
Position Surface form Disambiguated ID Type / Status
Subject Hubbard model E450154 entity
Predicate hasVariant P455 FINISHED
Object multi-orbital Hubbard model
The multi-orbital Hubbard model is an extension of the standard Hubbard model that includes multiple electronic orbitals per lattice site to study more realistic correlated electron systems and orbital-dependent phenomena.
E450154 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: multi-orbital Hubbard model | Statement: [Hubbard model, hasVariant, multi-orbital Hubbard model]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: multi-orbital Hubbard model
Context triple: [Hubbard model, hasVariant, multi-orbital Hubbard model]
  • A. Hubbard model
    The Hubbard model is a fundamental theoretical model in condensed matter physics that describes interacting electrons on a lattice and is widely used to study phenomena such as magnetism, metal–insulator transitions, and high-temperature superconductivity.
  • B. Dynamical Mean-Field Theory
    Dynamical Mean-Field Theory is a non-perturbative theoretical approach in condensed matter physics that captures local electronic correlations by mapping lattice models onto self-consistent quantum impurity problems, enabling the study of phenomena such as the Mott metal–insulator transition.
  • C. t-J model
    The t-J model is a theoretical framework in condensed matter physics used to describe strongly correlated electrons on a lattice, particularly in the study of high-temperature superconductivity.
  • D. Simons Collaboration on the Many Electron Problem
    The Simons Collaboration on the Many Electron Problem is a research initiative that brings together mathematicians and physicists to develop new theoretical and computational approaches for understanding complex many-electron systems in quantum mechanics and condensed matter physics.
  • E. Gutzwiller approximation
    The Gutzwiller approximation is a variational method in condensed matter physics used to study strongly correlated electron systems, particularly metal–insulator (Mott) transitions in lattice models like the Hubbard model.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: multi-orbital Hubbard model
Triple: [Hubbard model, hasVariant, multi-orbital Hubbard model]
Generated description
The multi-orbital Hubbard model is an extension of the standard Hubbard model that includes multiple electronic orbitals per lattice site to study more realistic correlated electron systems and orbital-dependent phenomena.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: multi-orbital Hubbard model
Target entity description: The multi-orbital Hubbard model is an extension of the standard Hubbard model that includes multiple electronic orbitals per lattice site to study more realistic correlated electron systems and orbital-dependent phenomena.
  • A. Hubbard model chosen
    The Hubbard model is a fundamental theoretical model in condensed matter physics that describes interacting electrons on a lattice and is widely used to study phenomena such as magnetism, metal–insulator transitions, and high-temperature superconductivity.
  • B. Dynamical Mean-Field Theory
    Dynamical Mean-Field Theory is a non-perturbative theoretical approach in condensed matter physics that captures local electronic correlations by mapping lattice models onto self-consistent quantum impurity problems, enabling the study of phenomena such as the Mott metal–insulator transition.
  • C. t-J model
    The t-J model is a theoretical framework in condensed matter physics used to describe strongly correlated electrons on a lattice, particularly in the study of high-temperature superconductivity.
  • D. Simons Collaboration on the Many Electron Problem
    The Simons Collaboration on the Many Electron Problem is a research initiative that brings together mathematicians and physicists to develop new theoretical and computational approaches for understanding complex many-electron systems in quantum mechanics and condensed matter physics.
  • E. Gutzwiller approximation
    The Gutzwiller approximation is a variational method in condensed matter physics used to study strongly correlated electron systems, particularly metal–insulator (Mott) transitions in lattice models like the Hubbard model.
  • F. None of above.

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69d8d381d6388190a9e94e9c658174e4 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e51b13ee88819091e7e007d17dcc73 completed April 19, 2026, 6:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a03f0ab8760819093e1132b91030b79 completed May 13, 2026, 3:31 a.m.
NEDg Description generation batch_6a03f24c7e248190a3330f339c0caded completed May 13, 2026, 3:38 a.m.
NED2 Entity disambiguation (via description) batch_6a03f2d8703881909b78c2377d9bb542 completed May 13, 2026, 3:41 a.m.
Created at: April 10, 2026, 11:24 a.m.